| Type: | Package |
| Encoding: | UTF-8 |
| Title: | Multivariate Adaptive Shrinkage |
| Version: | 0.2.79 |
| Date: | 2023-10-18 |
| Description: | Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <doi:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation. |
| URL: | https://github.com/stephenslab/mashr |
| BugReports: | https://github.com/stephenslab/mashr/issues |
| License: | BSD_3_clause + file LICENSE |
| Copyright: | file COPYRIGHTS |
| Depends: | R (≥ 3.3.0), ashr (≥ 2.2-22) |
| Imports: | assertthat, utils, stats, plyr, rmeta, Rcpp (≥ 1.0.8), mvtnorm, abind, softImpute |
| LinkingTo: | Rcpp, RcppArmadillo, RcppGSL (≥ 0.3.8) |
| Suggests: | MASS, REBayes, corrplot (≥ 0.90), testthat, kableExtra, knitr, rmarkdown, profmem, flashier, ebnm |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Biarch: | true |
| RoxygenNote: | 7.2.3 |
| Packaged: | 2023-10-18 13:05:18 UTC; pcarbo |
| Author: | Matthew Stephens [aut], Sarah Urbut [aut], Gao Wang [aut], Yuxin Zou [aut], Yunqi Yang [ctb], Sam Roweis [cph], David Hogg [cph], Jo Bovy [cph], Peter Carbonetto [aut, cre] |
| Maintainer: | Peter Carbonetto <peter.carbonetto@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-10-18 16:20:02 UTC |
Fit extreme deconvolution to mash data using Bovy et al 2011
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
bovy_wrapper(data, Ulist_init, subset = NULL, ...)
Arguments
data |
mash data object |
Ulist_init |
a list of covariance matrices to initialize to |
subset |
the indices of the observations to be used (defaults to all of them) |
... |
arguments to be passed to |
Details
This is a wrapper to ExtremeDeconvolution::extreme_deconvolution It fixes the projection to be the identity, and the means to be 0
Value
the fitted mixture: a list of mixture proportions and covariance matrices
Compute matrix of conditional likelihoods.
Description
computes matrix of condition likelihoods for each of J rows of Bhat for each of P prior covariances.
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
calc_lik_matrix(
data,
Ulist,
log = FALSE,
mc.cores = 1,
algorithm.version = c("Rcpp", "R")
)
Arguments
data |
A |
Ulist |
List containing the prior covariance matrices. |
log |
If |
mc.cores |
The argument supplied to
|
algorithm.version |
Indicate R or Rcpp version |
Value
J x P matrix of multivariate normal likelihoods, p(bhat | Ulist[p], V).
calc_lik_matrix_common_cov
Description
computes matrix of likelihoods for each of J rows of Bhat for each of P prior covariances; special case when standard errors and variances are all same across j.
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
calc_lik_matrix_common_cov(data, Ulist, log = FALSE)
Arguments
data |
a mash data object, eg as created by |
Ulist |
list of prior covariance matrices |
log |
if true computes log-likelihood |
Details
Compared with calc_lik_matrix this function
exploits fact that observations are iid in this case, so the
inverse covariance matrices only need to be done once, reducing
computation to R^3 + JR^2 instead of JR^3
Value
J x P vector of multivariate normal likelihoods, p(bhat | Ulist[p], V), where V is same for each bhat
Compute conditional likelihoods for bhat vector.
Description
Computes vector of likelihoods for bhat for each of P prior covariances.
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
calc_lik_vector(bhat, V, Ulist, log = FALSE)
Arguments
bhat |
bhat vector (length R) |
V |
R x R covariance matrix for likelihood. |
Ulist |
list of prior covariance matrices. |
log |
If |
Value
Vector of length P in which the pth element contains the multivariate normal likelihood p(bhat | Ulist[[p]], V).
Calculate matrix of relative likelihoods.
Description
Computes matrix of relative likelihoods for each of J rows of Bhat for each of P prior covariances.
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
calc_relative_lik_matrix(data, Ulist, algorithm.version = c("Rcpp", "R"))
Arguments
data |
A |
Ulist |
List containing the prior covariance matrices. |
algorithm.version |
indicates R or Rcpp |
Value
The return value is a list containing the following components:
lik_matrix |
J x P matrix containing likelihoods p(bhat[j] + Ulist[p], V), but normalized so that the largest entry in each row is 1. |
lfactors |
Vector which will recover the original
likelihoods; for example, |
Compute vector of alternative loglikelihoods from a matrix of log-likelihoods and fitted pi
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_alt_loglik_from_matrix_and_pi(pi_s, lm, Shat_alpha)
Arguments
pi_s |
the vector of mixture proportions, with first element corresponding to null |
lm |
the results of a likelihood matrix calculation from
|
Shat_alpha |
matrix of Shat^alpha |
Compute the total loglikelihood from a matrix of log-likelihoods and fitted pi
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_loglik_from_matrix_and_pi(pi_s, lm, Shat_alpha)
Arguments
pi_s |
the vector of mixture proportions |
lm |
the results of a likelihood matrix calculation from
|
Shat_alpha |
matrix of Shat^alpha |
Compute a vector of null loglikelihoods from a matrix of log-likelihoods
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_null_loglik_from_matrix(lm, Shat_alpha)
Arguments
lm |
the results of a likelihood matrix calculation from
|
Shat_alpha |
matrix of Shat^alpha |
Compute posterior matrices.
Description
Compute posterior matrices.
Usage
compute_posterior_matrices(
data,
Ulist,
posterior_weights,
algorithm.version = c("Rcpp", "R"),
A = NULL,
output_posterior_cov = FALSE,
mc.cores = 1,
posterior_samples = 0,
seed = 123
)
Arguments
data |
A |
Ulist |
List containing the prior covariance matrices. |
posterior_weights |
Vector containing the posterior probability of each mixture component in Ulist for the data. |
algorithm.version |
Indicates whether to use R or Rcpp version. |
A |
the linear transformation matrix, Q x R matrix. This is used to compute the posterior for Ab. |
output_posterior_cov |
whether or not to output posterior covariance matrices for all effects. |
mc.cores |
The argument supplied to
|
posterior_samples |
the number of samples to be drawn from the posterior distribution of each effect. |
seed |
a random number seed to use when sampling from the
posteriors. It is used when |
Value
The return value is a list containing the following components:
PosteriorMean |
J x Q matrix of posterior means. |
PosteriorSD |
J x Q matrix of posterior (marginal) standard deviations. |
NegativeProb |
J x Q matrix of posterior (marginal) probability of being negative. |
ZeroProb |
J x Q matrix of posterior (marginal) probability of being zero. |
lfsr |
J x Q matrix of local false sign rate estimates. |
lfdr |
J x Q matrix of local false discovery rate estimates. |
PosteriorCov |
Q x Q x J array of posterior covariance
matrices, if the |
PosteriorSamples |
M x Q x J array of samples, if the
|
Compute posterior matrices (when error covariance V_j is equal for all observations j)
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_posterior_matrices_common_cov_R(
data,
A,
Ulist,
posterior_weights,
output_posterior_cov = FALSE,
posterior_samples = 0,
seed = 123
)
Arguments
data |
a mash data object, eg as created by
|
A |
the linear transformation matrix, Q x R matrix. This is used to compute the posterior for Ab. |
Ulist |
a list of P covariance matrices for each mixture component |
posterior_weights |
the JxP posterior probabilities of each mixture component in Ulist for the data |
output_posterior_cov |
whether or not to output posterior covariance matrices for all effects |
posterior_samples |
the number of samples to be drawn from the posterior distribution of each effect. |
seed |
a random number seed to use when sampling from the
posteriors. It is used when |
Details
The computations are performed without allocating an excessive amount of memory.
Value
PosteriorMean JxQ matrix of posterior means
PosteriorSD JxQ matrix of posterior (marginal) standard deviations
NegativeProb JxQ matrix of posterior (marginal) probability of being negative
ZeroProb JxQ matrix of posterior (marginal) probability of being zero
lfsr JxQ matrix of local false sign rates
PosteriorCov QxQxJ array of posterior covariance matrices,
if the output_posterior_cov = TRUE
PosteriorSamples JxQxM array of samples, if the
posterior_samples = M > 0
Compute posterior matrices (general version)
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_posterior_matrices_general_R(
data,
A,
Ulist,
posterior_weights,
output_posterior_cov = FALSE,
posterior_samples = 0,
seed = 123
)
Arguments
data |
a mash data object, eg as created by
|
A |
the linear transformation matrix, Q x R matrix. This is used to compute the posterior for Ab. |
Ulist |
a list of P covariance matrices for each mixture component |
posterior_weights |
the JxP posterior probabilities of each mixture component in Ulist for the data |
output_posterior_cov |
whether or not to output posterior covariance matrices for all effects |
posterior_samples |
the number of samples to be drawn from the posterior distribution of each effect. |
seed |
a random number seed to use when sampling from the
posteriors. It is used when |
Details
The computations are performed without allocating an excessive amount of memory.
Value
PosteriorMean JxQ matrix of posterior means
PosteriorSD JxQ matrix of posterior (marginal) standard deviations
NegativeProb JxQ matrix of posterior (marginal) probability of being negative
ZeroProb JxQ matrix of posterior (marginal) probability of being zero
lfsr JxQ matrix of local false sign rates
PosteriorCov QxQxJ array of posterior covariance matrices,
if the output_posterior_cov = TRUE
PosteriorSamples JxQxM array of samples, if the
posterior_samples = M > 0
Compute posterior probabilities that each effect came from each component
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_posterior_weights(pi, lik_mat)
Arguments
pi |
a K vector of mixture proportions |
lik_mat |
a JxK matrix of likelihoods |
Value
a JxK matrix of posterior probabilities, the jth row contains posteriors for jth effect
Computes a vector of loglikelihoods from a matrix of log-likelihoods and fitted pi
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
compute_vloglik_from_matrix_and_pi(pi_s, lm, Shat_alpha)
Arguments
pi_s |
the vector of mixture proportions |
lm |
the results of a likelihood matrix calculation from
|
Shat_alpha |
matrix of Shat^alpha |
Create contrast matrix
Description
Create contrast matrix
Usage
contrast_matrix(R, ref, name = 1:R)
Arguments
R |
the number of column for the contrast matrix |
ref |
the reference group. It could be a number between 1,..., R, R is number of conditions, or the name of reference group. If there is no reference group, it can be the string 'mean'. |
name |
a length R vector contains the name for conditions |
Examples
contrast_matrix(5, 'mean')
Compute an R by R matrix of all 0s
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
cov_all_zeros(data)
Arguments
data |
a mash data object, eg as created by |
Value
a list with 1 entry, the R by R matrix of all 0s
Compute a list of canonical covariance matrices
Description
Compute a list of canonical covariance matrices
Usage
cov_canonical(
data,
cov_methods = c("identity", "singletons", "equal_effects", "simple_het")
)
Arguments
data |
a mash data object, eg as created by |
cov_methods |
a vector of strings indicating the matrices to
be used: "identity" for the identity (effects are independent among
conditions); "singletons" for the set of matrices with just one
non-zero entry |
Details
The default is that this function computes covariance matrices corresponding to the "bmalite" models.
Value
a list of covariance matrices
Examples
data = mash_set_data(Bhat = cbind(c(1,2),c(3,4)), Shat = cbind(c(1,1),c(1,1)))
cov_canonical(data)
cov_canonical(data,"singletons")
cov_canonical(data,c("id","sing")) # can use partial matching of names
Perform "extreme deconvolution" (Bovy et al) on a subset of the data
Description
Perform "extreme deconvolution" (Bovy et al) on a subset of the data
Usage
cov_ed(data, Ulist_init, subset = NULL, algorithm = c("bovy", "teem"), ...)
Arguments
data |
a mash data object |
Ulist_init |
a named list of covariance matrices to use to initialize ED; default is to use matrices from PCs |
subset |
a subset of data to be used when ED is run (set to NULL for all the data) |
algorithm |
algorithm to run ED |
... |
other arguments to be passed to ED algorith, see
|
Details
Runs the extreme deconvolution algorithm from Bovy et al
(Annals of Applied Statistics) to estimate data-driven covariance
matrices. It can be initialized with, for example running cov_pca with,
say, 5 PCs.
Examples
## Not run:
data = mash_set_data(Bhat = cbind(c(1,2),c(3,4)), Shat = cbind(c(1,1),c(1,1)))
U_pca = cov_pca(data,2)
U_x = apply(data$Bhat, 2, function(x) x - mean(x))
U_xx = t(U_x) %*% U_x / nrow(U_x)
cov_ed(data,c(U_pca, list(xx = U_xx)))
## End(Not run)
Compute an R by R matrix of all 1s
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
cov_equal_effects(data)
Arguments
data |
a mash data object, eg as created by |
Value
a list with 1 entry, the R by R matrix of all 1s
Compute all the singleton matrices corresponding to condition-specific effects in first condition only; used for testing purposes
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
cov_first_singleton(data)
Arguments
data |
a mash data object, eg as created by |
Value
an R by R matrix with all 0s except the (1,1) element is 1
Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices
Description
Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices
Usage
cov_flash(
data,
factors = c("default", "nonneg"),
subset = NULL,
remove_singleton = FALSE,
tag = NULL,
output_model = NULL,
greedy_args = list(),
backfit_args = list()
)
Arguments
data |
A “mash” data object. |
factors |
If |
subset |
Data samples (rows) used to estimate the
covariances. Sset to |
remove_singleton |
If |
tag |
How to name the covariance matrices. |
output_model |
The fitted flash model will be saved to this file
(using |
greedy_args |
List containing additional parameters passed to
|
backfit_args |
List containing additional parameters passed
to |
Value
A list of covariance matrices.
Examples
# See https://stephenslab.github.io/mashr/articles/flash_mash.html
# for an example
produce list of rank-1 covariance matrices corresponding to rows of f
Description
produce list of rank-1 covariance matrices corresponding to rows of f
Usage
cov_from_factors(f, name)
Arguments
f |
a matrix of factors (each row is a factor) |
name |
a string indicating the name to use |
Value
a list of rank one matrices whose kth element is f[k,] f[k,]' and named name_k
Perform PCA on data and return list of candidate covariance matrices
Description
Perform PCA on data and return list of candidate covariance matrices
Usage
cov_pca(data, npc, subset = NULL)
Arguments
data |
a mash data object |
npc |
the number of PCs to use |
subset |
indices of the subset of data to use (set to NULL for all data) |
Value
Returns a list of covariance matrices: the npc rank-one
covariance matrices based on the first npc PCs, and the rank npc
covariance matrix. If flashier did not identify any factors,
NULL is returned.
Examples
data = mash_set_data(Bhat = cbind(c(1,2),c(3,4)), Shat = cbind(c(1,1),c(1,1)))
cov_pca(data,2)
Compute covariance matrices with diagonal element 1 and off-diagonal element corr
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
cov_simple_het(data, corr = c(0.25, 0.5, 0.75))
Arguments
data |
a mash data object, eg as created by |
corr |
a vector containing the correlations to be used |
Value
a list of matrices, one for each value in corr
Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" models
Description
Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" models
Usage
cov_udi(data, model = udi_model_matrix(n_conditions(data)))
Arguments
data |
a mash data object, eg as created by |
model |
a model matrix with R columns, where R is the number of conditions in the data; each row should be a vector of length R with elements "U","D" and "I" indicating whether each effect is Unassociated, Directly associated or Indirectly associated |
Details
If model is specified then this returns the covariance matrices for those models. The default creates all possible models. For a desription of the "Unassociated", "Directly associated" and "Indirectly associated" models see Stephens M (2013), A unified framework for Association Analysis with Multiple Related Phenotypes, PloS ONE.
Value
a named list of covariance matrices
Examples
data = mash_set_data(Bhat = cbind(c(1,2),c(3,4)), Shat = cbind(c(1,1),c(1,1)))
cov_udi(data)
cov_udi(data,c('I','D'))
Computes the covariance matrix for a single UDI model
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
cov_udi_single(data, model)
Arguments
data |
a mash data object |
model |
a vector of length R of "U","D" and "I" |
Value
returns a named list of one element
Estimate null correlations (simple)
Description
Estimates a null correlation matrix from data using simple z score threshold
Usage
estimate_null_correlation_simple(data, z_thresh = 2, est_cor = TRUE)
Arguments
data |
a mash data object, eg as created by |
z_thresh |
the z score threshold below which to call an effect null |
est_cor |
whether to estimate correlation matrix (TRUE) or the covariance matrix (FALSE). |
Details
Returns a simple estimate of the correlation matrix (or covariance matrix) among conditions under the null. Specifically, the simple estimate is the empirical correlation (or covariance) matrix of the z scores for those effects that have (absolute) z score < z_thresh in all conditions.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
estimate_null_correlation_simple(data)
Create expanded list of covariance matrices expanded by grid
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
expand_cov(Ulist, grid, usepointmass = TRUE)
Arguments
Ulist |
a list of covarance matrices |
grid |
a grid of scalar values by which the covariance matrices are to be sc |
usepointmass |
if TRUE adds a point mass at 0 (null component) to the list |
Value
This takes the covariance matrices in Ulist and multiplies them by the grid values If usepointmass is TRUE then it adds a null component.
Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
Description
We present a general algorithm to infer a d-dimensional distribution function given a set of heterogeneous, noisy observations or samples. This algorithm reconstructs the error-deconvolved or 'underlying' distribution function common to all samples, even when the individual samples have unique error and missing-data properties. The underlying distribution is modeled as a mixture of Gaussians, which is completely general. Model parameters are chosen to optimize a justified, scalar objective function: the logarithm of the probability of the data under the error-convolved model, where the error convolution is different for each data point. Optimization is performed by an Expectation Maximization (EM) algorithm, extended by a regularization technique and 'split-and-merge' procedure. These extensions mitigate problems with singularities and local maxima, which are often encountered when using the EM algorithm to estimate Gaussian density mixtures.
Usage
extreme_deconvolution(
ydata,
ycovar,
xamp,
xmean,
xcovar,
projection = NULL,
weight = NULL,
fixamp = NULL,
fixmean = NULL,
fixcovar = NULL,
tol = 1e-06,
maxiter = 1e+09,
w = 0,
logfile = NULL,
splitnmerge = 0,
maxsnm = FALSE,
likeonly = FALSE,
logweight = FALSE
)
Arguments
ydata |
[ndata,dy] matrix of observed quantities |
ycovar |
[ndata,dy] / [ndata,dy,dy] / [dy,dy,ndata] matrix, list or 3D array of observational error covariances (if [ndata,dy] then the error correlations are assumed to vanish) |
xamp |
[ngauss] array of initial amplitudes (*not* [1,ngauss]) |
xmean |
[ngauss,dx] matrix of initial means |
xcovar |
[ngauss,dx,dx] list of matrices of initial covariances |
projection |
[ndata,dy,dx] list of projection matrices |
weight |
[ndata] array of weights to be applied to the data points |
fixamp |
(default=None) None, True/False, or list of bools |
fixmean |
(default=None) None, True/False, or list of bools |
fixcovar |
(default=None) None, True/False, or list of bools |
tol |
(double, default=1.e-6) tolerance for convergence |
maxiter |
(long, default= 10**9) maximum number of iterations to perform |
w |
(double, default=0.) covariance regularization parameter (of the conjugate prior) |
logfile |
basename for several logfiles (_c.log has output from the c-routine; _loglike.log has the log likelihood path of all the accepted routes, i.e. only parts which increase the likelihood are included, during splitnmerge) |
splitnmerge |
(int, default=0) depth to go down the splitnmerge path |
maxsnm |
(Bool, default=False) use the maximum number of split 'n' merge steps, K*(K-1)*(K-2)/2 |
likeonly |
(Bool, default=False) only compute the total log likelihood of the data |
logweight |
(bool, default=False) if True, weight is actually log(weight) |
Value
avgloglikedata |
avgloglikedata after convergence |
xamp |
updated xamp |
xmean |
updated xmean |
xcovar |
updated xcovar |
Author(s)
Jo Bovy, David W. Hogg, & Sam T. Roweis
References
Inferring complete distribution functions from noisy, heterogeneous and incomplete observations Jo Bovy, David W. Hogg, & Sam T. Roweis, Submitted to AOAS (2009) [arXiv/0905.2979]
Examples
## Not run:
ydata <-
c(2.62434536, 0.38824359, 0.47182825, -0.07296862, 1.86540763,
-1.30153870, 2.74481176, 0.23879310, 1.31903910, 0.75062962,
2.46210794, -1.06014071, 0.67758280, 0.61594565, 2.13376944,
-0.09989127, 0.82757179, 0.12214158, 1.04221375, 1.58281521,
-0.10061918, 2.14472371, 1.90159072, 1.50249434, 1.90085595,
0.31627214, 0.87710977, 0.06423057, 0.73211192, 1.53035547,
0.30833925, 0.60324647, 0.31282730, 0.15479436, 0.32875387,
0.98733540, -0.11731035, 1.23441570, 2.65980218, 1.74204416,
0.80816445, 0.11237104, 0.25284171, 2.69245460, 1.05080775,
0.36300435, 1.19091548, 3.10025514, 1.12015895, 1.61720311,
1.30017032, 0.64775015, -0.14251820, 0.65065728, 0.79110577,
1.58662319, 1.83898341, 1.93110208, 1.28558733, 1.88514116,
0.24560206, 2.25286816, 1.51292982, 0.70190717, 1.48851815,
0.92442829, 2.13162939, 2.51981682, 3.18557541, -0.39649633,
-0.44411380, 0.49553414, 1.16003707, 1.87616892, 1.31563495,
-1.02220122, 0.69379599, 1.82797464, 1.23009474, 1.76201118,
0.77767186, 0.79924193, 1.18656139, 1.41005165, 1.19829972,
1.11900865, 0.32933771, 1.37756379, 1.12182127, 2.12948391,
2.19891788, 1.18515642, 0.62471505, 0.36126959, 1.42349435,
1.07734007, 0.65614632, 1.04359686, 0.37999916, 1.69803203,
0.55287144, 2.22450770, 1.40349164, 1.59357852, -0.09491185,
1.16938243, 1.74055645, 0.04629940, 0.73378149, 1.03261455,
-0.37311732, 1.31515939, 1.84616065, 0.14048406, 1.35054598,
-0.31228341, 0.96130449, -0.61577236, 2.12141771, 1.40890054,
0.97538304, 0.22483838, 2.27375593, 2.96710175, -0.85798186,
2.23616403, 2.62765075, 1.33801170, -0.19926803, 1.86334532,
0.81907970, 0.39607937, -0.23005814, 1.55053750, 1.79280687,
0.37646927, 1.52057634, -0.14434139, 1.80186103, 1.04656730,
0.81343023, 0.89825413, 1.86888616, 1.75041164, 1.52946532,
1.13770121, 1.07782113, 1.61838026, 1.23249456, 1.68255141,
0.68988323, -1.43483776, 2.03882460, 3.18697965, 1.44136444,
0.89984477, 0.86355526, 0.88094581, 1.01740941, -0.12201873,
0.48290554, 0.00297317, 1.24879916, 0.70335885, 1.49521132,
0.82529684, 1.98633519, 1.21353390, 3.19069973, -0.89636092,
0.35308331, 1.90148689, 3.52832571, 0.75136522, 1.04366899,
0.77368576, 2.33145711, 0.71269214, 1.68006984, 0.68019840,
-0.27255875, 1.31354772, 1.50318481, 2.29322588, 0.88955297,
0.38263794, 1.56276110, 1.24073709, 1.28066508, 0.92688730,
2.16033857, 1.36949272, 2.90465871, 2.11105670, 1.65904980,
-0.62743834, 1.60231928, 1.42028220, 1.81095167, 2.04444209)
ydata <- matrix(ydata,length(ydata),1)
N <- dim(ydata)[1]
ycovar <- ydata*0 + 0.01
xamp <- c(0.5,0.5)
xmean <- matrix(c(0.86447943, 0.67078879, 0.322681, 0.45087394),2,2)
xcovar <-
list(matrix(c(0.03821028, 0.04014796, 0.04108113, 0.03173839),2,2),
matrix(c(0.06219194, 0.09738021, 0.04302473, 0.06778009),2,2))
projection <- list()
for (i in 1:N)
projection[[i]] = matrix(c(i%%2,(i+1)%%2),1,2)
res <- extreme_deconvolution(ydata, ycovar, xamp, xmean, xcovar,
projection=projection, logfile="ExDeconDemo")
## End(Not run)
Return the estimated mixture proportions
Description
Return the estimated mixture proportions
Usage
get_estimated_pi(m, dimension = c("cov", "grid", "all"))
Arguments
m |
the mash result |
dimension |
indicates whether you want the mixture proportions for the covariances, grid, or all |
Details
If the fit was done with 'usepointmass=TRUE' then the first element of the returned vector will correspond to the null, and the remaining elements to the non-null covariance matrices. Suppose the fit was done with $K$ covariances and a grid of length $L$. If 'dimension=cov' then the returned vector will be of length $K$ (or $K+1$ if 'usepointmass=TRUE'). If 'dimension=grid' then the returned vector will be of length $L$ (or $L+1$). If 'dimension=all' then the returned vector will be of length $LK$ (or $LK+1$). The names of the vector will be informative for which combination each element corresponds to.
Value
a named vector containing the estimated mixture proportions.
Return the Bayes Factor for each effect
Description
Return the Bayes Factor for each effect
Usage
get_log10bf(m)
Arguments
m |
the mash result (from joint or 1by1 analysis); must have been computed using usepointmass=TRUE |
Value
if m was fitted using usepointmass=TRUE then returns a vector of the log10(bf) values for each effect. That is, the jth element lbf[j] is log10(Pr(Bj | g=ghat-nonnull)/Pr(Bj | g = 0)) where ghat-nonnull is the non-null part of ghat. Otherwise returns NULL.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
get_log10bf(m)
Count number of conditions each effect is significant in
Description
Count number of conditions each effect is significant in
Usage
get_n_significant_conditions(
m,
thresh = 0.05,
conditions = NULL,
sig_fn = get_lfsr
)
Arguments
m |
the mash result (from joint or 1by1 analysis) |
thresh |
indicates the threshold below which to call signals significant |
conditions |
which conditions to include in check (default to all) |
sig_fn |
the significance function used to extract significance from mash object; eg could be ashr::get_lfsr or ashr::get_lfdr |
Value
a vector containing the number of significant conditions
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
get_n_significant_conditions(m)
Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean
Description
Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean
Usage
get_pairwise_sharing(m, factor = 0.5, lfsr_thresh = 0.05, FUN = identity)
Arguments
m |
the mash fit |
factor |
a number in [0,1] the factor within which effects are considered to be shared |
lfsr_thresh |
the lfsr threshold for including an effect in the assessment |
FUN |
a function to be applied to the estimated effect sizes before assessing sharing. The most obvious choice beside the default 'FUN=identity' would be 'FUN=abs' if you want to ignore the sign of the effects when assesing sharing. |
Details
For each pair of tissues, first identify the effects that are significant (by lfsr<lfsr_thresh) in at least one of the two tissues. Then compute what fraction of these have an estimated (posterior mean) effect size within a factor 'factor' of one another. The results are returned as an R by R matrix.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
get_pairwise_sharing(m) # sharing by magnitude (same sign)
get_pairwise_sharing(m, factor=0) # sharing by sign
get_pairwise_sharing(m, FUN=abs) # sharing by magnitude when sign is ignored
Compute the proportion of (significant) signals shared by magnitude in each pair of conditions
Description
Compute the proportion of (significant) signals shared by magnitude in each pair of conditions
Usage
get_pairwise_sharing_from_samples(
m,
factor = 0.5,
lfsr_thresh = 0.05,
FUN = identity
)
Arguments
m |
the mash fit with samples from posteriors |
factor |
a number in [0,1] the factor within which effects are considered to be shared |
lfsr_thresh |
the lfsr threshold for including an effect in the assessment |
FUN |
a function to be applied to the estimated effect sizes before assessing sharing. The most obvious choice beside the default 'FUN=identity' would be 'FUN=abs' if you want to ignore the sign of the effects when assesing sharing. |
Details
For each pair of conditions, compute the fraction of effects that are within a factor 'factor' of one another. The results are returned as an R by R matrix.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data), posterior_samples=5, algorithm='R')
get_pairwise_sharing_from_samples(m) # sharing by magnitude (same sign)
get_pairwise_sharing_from_samples(m, factor=0) # sharing by sign
get_pairwise_sharing_from_samples(m, FUN=abs) # sharing by magnitude when sign is ignored
Return samples from a mash object
Description
Return samples from a mash object
Usage
get_samples(m)
Arguments
m |
The mash fit. |
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data), posterior_samples=5, algorithm='R')
get_samples(m)
Find effects that are significant in at least one condition
Description
Find effects that are significant in at least one condition
Usage
get_significant_results(m, thresh = 0.05, conditions = NULL, sig_fn = get_lfsr)
Arguments
m |
the mash result (from joint or 1by1 analysis) |
thresh |
indicates the threshold below which to call signals significant |
conditions |
which conditions to include in check (default to all) |
sig_fn |
the significance function used to extract significance from mash object; eg could be ashr::get_lfsr or ashr::get_lfdr. (Small values must indicate significant.) |
Value
a vector containing the indices of the significant effects, by order of most significant to least
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
get_significant_results(m)
Create names for covariance matrices
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Adds _suffixes to names for each element of suffixes
Usage
make_names(names, suffixes)
Arguments
names |
a string |
suffixes |
Apply mash method to data
Description
Apply mash method to data
Usage
mash(
data,
Ulist = NULL,
gridmult = sqrt(2),
grid = NULL,
normalizeU = TRUE,
usepointmass = TRUE,
g = NULL,
fixg = FALSE,
prior = c("nullbiased", "uniform"),
nullweight = 10,
optmethod = c("mixSQP", "mixIP", "mixEM", "cxxMixSquarem"),
control = list(),
verbose = TRUE,
add.mem.profile = FALSE,
algorithm.version = c("Rcpp", "R"),
pi_thresh = 1e-10,
A = NULL,
posterior_samples = 0,
seed = 123,
outputlevel = 2,
output_lfdr = FALSE
)
Arguments
data |
a mash data object containing the Bhat matrix, standard
errors, alpha value; created using |
Ulist |
a list of covariance matrices to use
(see |
gridmult |
scalar indicating factor by which adjacent grid values should differ; close to 1 for fine grid |
grid |
vector of grid values to use (scaling factors omega in paper) |
normalizeU |
whether or not to normalize the U covariances to have maximum of 1 on diagonal |
usepointmass |
whether to include a point mass at 0, corresponding to null in every condition |
g |
the value of g obtained from a previous mash fit - an alternative to supplying Ulist, grid and usepointmass |
fixg |
if g is supplied, allows the mixture proportions to be fixed rather than estimated; e.g., useful for fitting mash to test data after fitting it to training data |
prior |
indicates what penalty to use on the likelihood, if any |
nullweight |
scalar, the weight put on the prior under “nullbiased” specification, see “prior”. |
optmethod |
name of optimization method to use |
control |
A list of control parameters passed to optmethod. |
verbose |
If |
add.mem.profile |
If |
algorithm.version |
Indicates whether to use R or Rcpp version |
pi_thresh |
threshold below which mixture components are ignored in computing posterior summaries (to speed calculations by ignoring negligible components) |
A |
the linear transformation matrix, Q x R matrix. This is used to compute the posterior for Ab. |
posterior_samples |
the number of samples to be drawn from the posterior distribution of each effect. |
seed |
A random number seed to use when sampling from the
posteriors. It is used when |
outputlevel |
controls amount of computation / output; 1: output only estimated mixture component proportions, 2: and posterior estimates, 3: and posterior covariance matrices, 4: and likelihood matrices |
output_lfdr |
If |
Value
a list with elements result, loglik and fitted_g
Examples
Bhat = matrix(rnorm(100),ncol=5) # create some simulated data
Shat = matrix(rep(1,100),ncol=5)
data = mash_set_data(Bhat,Shat, alpha=1)
U.c = cov_canonical(data)
res.mash = mash(data,U.c)
# Run mash with penalty exponent on null term equal to 100.
# See "False disovery rates: a new deal" (M. Stephens 2017),
# supplementary material S.2.5 for more details.
set.seed(1)
simdata = simple_sims(500,5,1)
data = mash_set_data(simdata$Bhat,simdata$Shat)
U.c = cov_canonical(data)
res0 = mash(data,U.c)
res1 = mash(data,U.c,prior = "nullbiased",nullweight = 101)
plot(res0$fitted_g$pi,res1$fitted_g$pi,pch = 20)
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")
Perform condition-by-condition analyses
Description
Performs simple "condition-by-condition" analysis by
running ash from package ashr on data from each
condition, one at a time. May be a useful first step to identify
top hits in each condition before a mash analysis.
Usage
mash_1by1(data, alpha = 0, ...)
Arguments
data |
A list with the following two elements: |
alpha |
Numeric value of alpha parameter in the model. alpha = 0 for Exchangeable Effects (EE), alpha = 1 for Exchangeable Z-scores (EZ). |
... |
optionally, other parameters to be passed to ash |
Value
A list similar to the output of mash, particularly including posterior matrices.
Examples
simdata = simple_sims(50,5,1)
mash_1by1(simdata)
Compute loglikelihood for fitted mash object on new data.
Description
Compute loglikelihood for fitted mash object on new data.
Usage
mash_compute_loglik(g, data, algorithm.version = c("Rcpp", "R"))
Arguments
g |
A mash object or the fitted_g from a mash object. |
data |
A set of data on which to compute the loglikelihood. |
algorithm.version |
Indicate R or Rcpp version |
Details
The log-likelihood for each element is p(Bhat_j |
Shat_j,g,\alpha) where Bhat_j | B_j, Shat_j \sim N(B_j,
Shat_j) and B_j/Shat_j^\alpha | Shat_j \sim g.
Value
The log-likelihood for data computed using g.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
mash_compute_loglik(m,data)
Compute posterior matrices for fitted mash object on new data
Description
Compute posterior matrices for fitted mash object on new data
Usage
mash_compute_posterior_matrices(
g,
data,
pi_thresh = 1e-10,
algorithm.version = c("Rcpp", "R"),
A = NULL,
output_posterior_cov = FALSE,
posterior_samples = 0,
seed = 123
)
Arguments
g |
a mash object or the fitted_g from a mash object. |
data |
a set of data on which to compute the posterior matrices |
pi_thresh |
threshold below which mixture components are ignored in computing posterior summaries (to speed calculations by ignoring negligible components) |
algorithm.version |
Indicates whether to use R or Rcpp version |
A |
the linear transformation matrix, Q x R matrix. This is used to compute the posterior for Ab. |
output_posterior_cov |
whether or not to output posterior covariance matrices for all effects |
posterior_samples |
the number of samples to be drawn from the posterior distribution of each effect. |
seed |
a random number seed to use when sampling from the
posteriors. It is used when |
Value
A list of posterior matrices
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
mash_compute_posterior_matrices(m,data)
Compute vector of loglikelihood for fitted mash object on new data
Description
Compute vector of loglikelihood for fitted mash object on new data
Usage
mash_compute_vloglik(g, data, algorithm.version = c("Rcpp", "R"))
Arguments
g |
A mash object. |
data |
A set of data on which to compute the loglikelihood. |
algorithm.version |
Indicate R or Rcpp version |
Details
The log-likelihood for each element is p(Bhat_j |
Shat_j,g,\alpha) where Bhat_j | B_j, Shat_j \sim N(B_j,
Shat_j) and B_j/Shat_j^\alpha | Shat_j \sim g Here the value
of \alpha is set when setting up the data object in
'mash_set_data'. If g is a mash object (safest!) then the function
will check that this value matches the \alpha used when
fitting 'mash'. Note: as a convenience, this function can also be
called with g a mixture distribution with same structure as the
fitted_g from a mash object. This is mostly useful when doing
simulations, where you might want to compute the likelihood under
the "true" g. When used in this way the user is responsible for
making sure that the g makes sense with the alpha set in data.
Value
The vector of log-likelihoods for each data point computed using g.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
mash_compute_vloglik(m,data)
Fit mash model and estimate residual correlations using EM algorithm
Description
Estimates a residual correlation matrix from data using an ad hoc EM algorithm.
Usage
mash_estimate_corr_em(
data,
Ulist,
init,
max_iter = 30,
tol = 1,
est_cor = TRUE,
track_fit = FALSE,
prior = c("nullbiased", "uniform"),
details = TRUE,
...
)
Arguments
data |
a mash data object, eg as created by |
Ulist |
a list of covariance matrices to use |
init |
the initial value for the residual correlation. If it is
not given, we use result from
|
max_iter |
maximum number of iterations to perform |
tol |
convergence tolerance |
est_cor |
whether to estimate correlation matrix (TRUE) or the covariance matrix (FALSE) |
track_fit |
add an attribute |
prior |
indicates what penalty to use on the likelihood, if any |
details |
whether to return details of the model, if it is TRUE, the mash model, the number of iterations and the value of objective functions will be returned |
... |
other parameters pass to |
Details
Returns the estimated residual correlation matrix among conditions. We estimate the residual correlation matrix using an ad hoc em algorithm. The update in the ad hoc M step is not guaranteed to increase the likelihood, therefore, the EM algorithm is stopped before the likelihood drops. The residual correlation matrix V is estimated using the posterior second moment of the noise.
Warning: This method could take some time. The
estimate_null_correlation_simple gives a quick
approximation for the null correlation matrix.
Value
the estimated correlation matrix and the
fitted mash model
V |
estimated residual correlation matrix |
mash.model |
fitted mash model |
Examples
simdata = simple_sims(100,5,1)
m.1by1 = mash_1by1(mash_set_data(simdata$Bhat,simdata$Shat))
strong.subset = get_significant_results(m.1by1,0.05)
random.subset = sample(1:nrow(simdata$Bhat),20)
data.strong = mash_set_data(simdata$Bhat[strong.subset,], simdata$Shat[strong.subset,])
data.tmp = mash_set_data(simdata$Bhat[random.subset,], simdata$Shat[random.subset,])
U_pca = cov_pca(data.strong, 3)
U_ed = cov_ed(data.strong, U_pca)
Vhat = mash_estimate_corr_em(data.tmp, U_ed)
Plot metaplot for an effect based on posterior from mash
Description
Plot metaplot for an effect based on posterior from mash
Usage
mash_plot_meta(m, i, xlab = "Effect size", ylab = "Condition", ...)
Arguments
m |
the result of a mash fit |
i |
index of the effect to plot |
xlab |
Character string specifying x-axis label. |
ylab |
Character string specifying y-axis label. |
... |
Additional arguments passed to |
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
mash_plot_meta(m,1)
Create a data object for mash analysis.
Description
Create a data object for mash analysis.
Usage
mash_set_data(
Bhat,
Shat = NULL,
alpha = 0,
df = Inf,
pval = NULL,
V = diag(ncol(Bhat)),
zero_check_tol = .Machine$double.eps,
zero_Bhat_Shat_reset = 0,
zero_Shat_reset = 0
)
Arguments
Bhat |
An N by R matrix of observed estimates. |
Shat |
An N by R matrix of corresponding standard errors. Shat can be a scalar if all standard errors are equal. This is most useful if Bhat is a matrix of Z scores, so elements of Shat are all 1. Default is 1. |
alpha |
Numeric value of alpha parameter in the model. alpha = 0 for Exchangeable Effects (EE), alpha = 1 for Exchangeable Z-scores (EZ). Default is 0. Please refer to equation (3.2) of M. Stephens 2016, Biostatistics for a discussion on alpha. |
df |
An N by R matrix of corresponding degrees of freedom of the t-statistic Bhat/Shat. Can be a scalar if all degrees of freedom are equal. Default is inf (for large samples). |
pval |
An N by R matrix of p-values of t-statistic Bhat/Shat. Shat and df should not be specified when pval is provided. |
V |
an R by R matrix / [R x R x N] array of effect specific correlation matrix of error correlations; must be positive definite. [So Bhat_j distributed as N(B_j,diag(Shat_j) V[,,j] diag(Shat_j)) where _j denotes the jth row of a matrix]. Defaults to identity. |
zero_check_tol |
a small positive number as threshold for Shat to be considered zero if any Shat is smaller or equal to this number. |
zero_Bhat_Shat_reset |
Replace zeros in Shat matrix to given value if the corresponding Bhat are also zeros. |
zero_Shat_reset |
Replace zeros in Shat matrix to given value. |
Value
A data object for passing into mash functions.
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
Create a data object for mash contrast analysis
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
mash_set_data_contrast(mashdata, L)
Arguments
mashdata |
a mash data object containing the Bhat matrix,
standard errors, V; created using |
L |
the contrast matrix |
Value
a data object after the contrast transfermation
Update the data object for mash analysis.
Description
This function can update two parts of the mash data. The first one is setting the reference group, so the mash data can be used for commonbaseline analysis. The other one is updating the null correlation matrix.
Usage
mash_update_data(mashdata, ref = NULL, V = NULL)
Arguments
mashdata |
mash data object ontaining the Bhat matrix,
standard errors, V; created using |
ref |
the reference group. It could be a number between 1,..., R, R is number of conditions, or the name of reference group. If there is no reference group, it can be the string 'mean'. |
V |
an R by R matrix / [R x R x N] array of correlation matrix of error correlations |
Value
a updated mash data object
Examples
simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
mash_update_data(data, 'mean')
Estimate mixture weights by maximum (penalized) likelihood
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
optimize_pi(
matrix_lik,
pi_init = NULL,
prior = NULL,
optmethod = c("mixSQP", "mixIP", "mixEM", "cxxMixSquarem"),
control = list()
)
Arguments
matrix_lik |
a matrix of likelihoods, where the (i,k)th entry is the probability of observation i given it came from component k of g |
pi_init |
numeric vector specifying value from which to initialize optimization |
prior |
numeric vector specifying prior to use in the penalized likelihood |
optmethod |
a string, giving name of optimization function to use |
control |
a list of parameters to be passed to optmethod |
Value
numeric vector specifying the optimal mixture weights
posterior_cov
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
posterior_cov(Vinv, U)
Arguments
Vinv |
R x R inverse covariance matrix for the likelihood |
U |
R x R prior covariance matrix |
Details
If bhat is N(b,V) and b is N(0,U) then b|bhat N(mu1,U1). This function returns U1.
Value
R x R posterior covariance matrix
posterior_mean
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
posterior_mean(bhat, Vinv, U1)
Arguments
bhat |
R vector of observation |
Vinv |
R x R inverse covariance matrix for the likelihood |
U1 |
R x R posterior covariance matrix, computed using posterior_cov |
Details
If bhat is N(b,V) and b is N(0,U) then b|bhat N(mu1,U1). This function returns mu1.
Value
R vector of posterior mean
posterior_mean_matrix
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
posterior_mean_matrix(Bhat, Vinv, U1)
Arguments
Bhat |
J by R matrix of observations |
Vinv |
R x R inverse covariance matrix for the likelihood |
U1 |
R x R posterior covariance matrix, computed using posterior_cov |
Details
Computes posterior mean under multivariate normal model for each row of matrix Bhat. Note that if bhat is N_R(b,V) and b is N_R(0,U) then b|bhat N_R(mu1,U1). This function returns a matrix with jth row equal to mu1(bhat) for bhat= Bhat[j,].
Value
R vector of posterior mean
Scale each covariance matrix in list Ulist by a scalar in vector grid
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
scale_cov(Ulist, grid)
Arguments
Ulist |
a list of matrices |
grid |
a vector of scaling factors (standard deviaions) |
Value
a list with length length(Ulist)*length(grid), with values grid[i]^2*Ulist[[j]]
Create simplest simulation, cj = mu 1 data used for contrast analysis
Description
Create simplest simulation, cj = mu 1 data used for contrast analysis
Usage
sim_contrast1(nsamp = 100, ncond = 5, err_sd = sqrt(0.5))
Arguments
nsamp |
number of samples of each type |
ncond |
number of conditions |
err_sd |
the standard deviation of the errors |
Details
There is no true deviation exists in this case
Examples
sim_contrast1(100,5)
Create simulation with signal data used for contrast analysis.
Description
Create simulation with signal data used for contrast analysis.
Usage
sim_contrast2(nsamp = 1000, ncond = 5, err_sd = sqrt(0.5))
Arguments
nsamp |
Number of samples of each type. |
ncond |
Number of conditions. |
err_sd |
The standard deviation of the errors. |
Details
The first condition is the reference group. The deviations are the difference between the subsequent conditions with the reference group. The simulation consists of 90 10 different types of deviations: equal among conditions, present only in the first subsequent condition, independent across conditions.
Examples
sim_contrast2(100,5)
Create some simple simulated data for testing purposes
Description
Create some simple simulated data for testing purposes
Usage
simple_sims(nsamp = 100, ncond = 5, err_sd = 0.01)
Arguments
nsamp |
number of samples of each type |
ncond |
number of conditions |
err_sd |
the standard deviation of the errors |
Details
The simulation consists of equal numbers of four different types of effects: null, equal among conditions, present only in first condition, independent across conditions
Examples
simple_sims(100, 5)
Create some more simple simulated data for testing purposes
Description
Create some more simple simulated data for testing purposes
Usage
simple_sims2(nsamp = 100, err_sd = 0.01)
Arguments
nsamp |
number of samples of each type |
err_sd |
the standard deviation of the errors |
Details
The simulation consists of five conditions with two types of effecc those present (and identical) in first two conditions and those present (and identical) in last three conditions
Examples
simple_sims2(100, 5)
Fit extreme deconvolution to mash data using TEEM method developed by Y. Yang and M Stephens
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
teem_wrapper(
data,
Ulist_init,
subset = NULL,
w_init = NULL,
maxiter = 5000,
converge_tol = 1e-07,
eigen_tol = 1e-07,
verbose = FALSE
)
Arguments
data |
mash data object |
Ulist_init |
a list of covariance matrices to initialize to |
subset |
the indices of the observations to be used (defaults to all of them) |
Value
the fitted mixture: a list of mixture proportions and covariance matrices
Create a matrix whose rows contain all possible combinations of the U,D,I models that are allowed.
Description
This is an internal (non-exported) function. This help page provides additional documentation mainly intended for developers and expert users.
Usage
udi_model_matrix(R)
Arguments
R |
the number of conditions |
Value
a matrix that is Nmodel by R with each row containing a vector of "U", "D" and "I" characters corresponding to a valide model. Constraint is that there is at least one "D" in each row